A study of multiple attributes decision making methods facing uncertain attributes

Abstract

Many decision-making methods have been developed to help decision makers (DMs) make efficient decisions. One decision making method involves selecting the best choice among alternatives based on a set of criteria. Multiple Attribute Decision-Making (MADM) methods allow opportunities to determine the optimal alternative based on multiple attributes. This research aims to overcome two concerns in current MADM methods: uncertainty of attributes and sensitivity of ranking results. Based on availability of information for attributes, a DM maybe certain or uncertain on his judgment on alternatives. Researchers have introduced the use of linguistic terms or uncertain intervals to tackle the uncertainty problems. This study provides an integrated approach to model uncertainty in one of the most popular MADM methods: TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Current MADM methods also provide a final ranking of alternatives under consideration and, the final solution is based on a calculated number assigned to each alternative. Results have shown that the final value of alternatives may be close to each other uncertain attributes, but current methods rank alternatives according to the final scores. It exhibits a sensitivity issue related to formation of the ranking list. The proposed method solves this problem by simulating random numbers within uncertain intervals in the decision matrix. The proposed outcome is a ranking distribution for alternatives. The proposed method is based on TOPSIS, which defines the best and the worst solution for each attribute and defines the best alternative as closest to best and farthest from the worst solution. Random number distributions were studied under the proposed simulation solution approach. Result showed that triangular random number distribution provides better ranking results than uniform distribution. A case study of building design selection considering resiliency and sustainability attributes was presented to demonstrate use of the proposed method. The study demonstrated that proposed method can provide better decision option for designers due to the ability to consider uncertain attributes. In addition using the proposed method, a DM can observe the final ranking distribution resulted from uncertain attribute values.